2 citations · 3 across the 3 of their papers we have counts for
3 papers
GITO: Graph-Informed Transformer Operator for Learning Complex Partial Differential Equations
Milad Ramezankhani, Janak M. Patel, Anirudh Deodhar +1
We present a novel graph-informed transformer operator (GITO) architecture for learning complex partial differential equation systems defined on irregular geometries and non-unifor…
HyperLoRA for PDEs
Ritam Majumdar, Vishal Jadhav, Anirudh Deodhar +3
Physics-informed neural networks (PINNs) have been widely used to develop neural surrogates for solutions of Partial Differential Equations. A drawback of PINNs is that they have t…
Symbolic Regression for PDEs using Pruned Differentiable Programs
Ritam Majumdar, Vishal Jadhav, Anirudh Deodhar +3
Physics-informed Neural Networks (PINNs) have been widely used to obtain accurate neural surrogates for a system of Partial Differential Equations (PDE). One of the major limitatio…